# Using machine learning to understand physics graduate school admissions

## TL;DR

This study uses machine learning to analyze physics graduate admissions data, revealing that undergraduate GPA and GRE scores can predict admission outcomes with 75% accuracy, highlighting factors influencing diversity.

## Contribution

The paper demonstrates the application of machine learning models to predict graduate admissions and identifies key factors affecting admission decisions in physics PhD programs.

## Key findings

- GPA and GRE scores are strong predictors of admission
- Machine learning achieves 75% prediction accuracy
- Insights into admissions criteria can inform diversity efforts

## Abstract

Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of female and racial minority students has remained unchanged for the past 20 years. The current graduate program admissions process can create challenges for achieving diversity goals in physics. In this paper, we will investigate how the various aspects of a prospective student's application to a physics doctoral program affect the likelihood the applicant will be admitted. Admissions data was collected from a large, Midwestern public research university that has a decentralized admissions process and included applicants' undergraduate GPAs and institutions, research interests, and GRE scores. Because the collected data varied in scale, we used supervised machine learning algorithms to create models that predict who was admitted into the PhD program. We find that using only the applicant's undergraduate GPA and physics GRE score, we are able to predict with 75% accuracy who will be admitted to the program.